Control device and method for calculating an output parameter for a controller

ABSTRACT

A control device in a vehicle includes a unit for calculating, during operation of the vehicle, on the basis of at least one input variable ascertained during operation, at least one output variable for a control system of functions of the vehicle. The control device performs the calculation of the output variables using a Bayesian regression of training values ascertained, before operation, for the output variable and the input variable.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of U.S. patentapplication Ser. No. 12/384,874, which was filed Apr. 8, 2009, issued onApr. 10, 2012 as U.S. Pat. No. 8,155,857, and claims priority under 35U.S.C. § 119 to DE 10 2008 001 081.2, filed in the Federal Republic ofGermany on Apr. 9, 2008.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a control device in a vehicle and amethod for calculating an output variable for a control system offunctions of the vehicle.

2. Description of the Related Art

A variety of methods can be used in order to determine variables invehicle control devices that cannot be measured or can be measured onlywith great difficulty (and thus too expensively for implementation inproduction vehicles), for example critical variables of the combustionprocess in an engine control device such as exhaust gas temperature,fill state, raw emissions values, efficiencies, consumption, etc., butthat are needed by the control device in order to perform its controlfunctions. One widely used method is that of characteristic curves,which can represent a one-dimensional correlation; or that ofcharacteristics diagrams, which can represent a two- ormulti-dimensional correlation. These characteristics diagrams can bedefined/stored via interpolation points, and a prediction of the targetvariable for specific input values can be interpolated from the adjacentinterpolation points, e.g. linearly or using splines (see, for example,published German patent application document DE 199 63 213 A1). Othermethods are based on, as a rule, highly simplified physical models (see,for example, published German patent application document DE 10 2008 004362 A1), which are often also represented by characteristics diagrams.Consideration is also given to data-based parametric regression modelssuch as, for example, neural networks (e.g. published German patentapplication document DE 10 2007 008 514).

In the automotive sector, so-called Bayesian regressions are used not“online” (i.e. during normal operation of the vehicle), but instead“offline,” for example in a calibration phase of an engine; see, forexample, “Bayesian statistics in engine mapping,” Ward, M. C., Brace, C.J., Vaughan, N. D., Shaddick, G., Ceen, R., Hale, T., Kennedy, G. inInternational Conference on Statistics and Analytical Methods inAutomotive Engineering, IMechE Conference Transactions, pp. 3-15, 2002;and “Validation of Neural Networks in Automotive Engine Calibration,” D.Lowe and K. Zapart, Proceedings Conference on Artificial NeuralNetworks, 1997.

When characteristics diagrams are used to characterize the correlations,there is often a high degree of application complexity or low prognosisaccuracy for multi-dimensional correlations. Creation of a reliablephysical model results in a large development outlay, and it is notalways possible to develop a physical model that is not too highlysimplified, in particular given the complex events of the combustionprocess, which must embrace not only thermodynamics but also, forexample, chemistry and flow mechanics. For the known methods, it is thecase that they make no statements as to the expected accuracy. This canbe important, however, in particular for critical target variables, inorder to ensure a reliable open- or closed-loop control strategy.

BRIEF SUMMARY OF THE INVENTION

Bayesian regression is intended to be used for the determination ofvariables in vehicle control devices that cannot be measured or can bemeasured only with great difficulty (and thus too expensively forimplementation in production vehicles), for example critical variablesof the combustion process in an engine control device such as exhaustgas temperature, fill state, raw emissions values, efficiencies,consumption, etc., but that are needed by the control device in order toperform its control functions. The method proposed here, usingnonparametric data-based Bayesian regression, is real-time-capable and,especially in the case of multidimensional and/or particularly complexproblems, supplies more reliable predictions that the known methods suchas, for example, characteristics diagram interpolation or simplifiedphysical models, and can moreover also be carried out with greatermemory efficiency. In addition, the necessary prior knowledge or,associated therewith, the required advance outlay for correctly definingthe input/output correlation using characteristics diagrams and/or aphysical model, or for defining algorithm parameters in the case ofparametric data-based regression methods such as neural networks, e.g.the number of neurons required, is considerably less with the proposedmethod and control device.

It is particularly advantageous to implement the Bayesian regression asGaussian processes, in particular sparse Gaussian processes, which canmake possible particularly memory- and computation-efficientcalculation.

For open- or closed-loop control on the basis of output variablesdetermined by Bayesian regression, it is particularly useful to takeinto account not only the output variable but also the varianceascertainable from the Bayesian regression. More reliable and moreaccurate open- and closed-loop control can thereby be achieved. Inparticular, with a high variance that, for example, exceeds a specificthreshold value, i.e. in the context of a possibly high inaccuracy forthe output variable, a defensive open- or closed-loop control strategycan be selected, a fault signal can be outputted, or other suitableactions can be taken.

Specific closed- and open-loop control tasks can be carried out,particularly advantageously, if older input variables and/or olderoutput variables are also incorporated into the regression, in additionto the current input variables, in order to determine the current outputvariable. The result is that even more-complex dynamic, i.e. notquasi-steady-state, processes can be mapped, and can be regulated orcontrolled with greater accuracy. The term used in this case is “dynamicmodeling by autoregression.”

In order to avoid excessive loads on the computation capacity andcapabilities of a computation unit of the control device, individualsub-tasks of the regression calculation can be offloaded to hardwareunits specializing therein. Because of their specialization, thesehardware units are capable of carrying out those tasks more quickly andmore efficiently than the more flexible computation units. For theBayesian regression that is to be carried out, it is particularlyadvantageous in this context if only exponential function calculationsthat require complex calculation are offloaded, by calculation units(such as microcontrollers) usually used in the control device, at leastpartly from the calculation unit to the hardware unit, in particular toa logic circuit.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically shows a control device as well as units external tothe control device.

FIG. 2a schematically shows a non-dynamic modeling operation.

FIG. 2b schematically shows a dynamic modeling operation.

FIG. 3a schematically shows a closed-/open-loop control system using anoutput variable.

FIG. 3b schematically shows a closed-/open-loop control system using anoutput variable, and the variance thereof.

FIG. 4 schematically shows components of a microcontroller of a controldevice and its connections.

FIG. 5 shows a method for determining output variables on the basis ofinput variables and a training variable, using Bayesian regression.

DETAILED DESCRIPTION OF THE INVENTION

So-called nonparametric regression methods have been developed andrefined in recent years, in particular kernel-based methods such assupport-vector machines [Support-Vector Networks, C. Cortes and V.Vapnik, Machine Learning, 20, 1995] and, as a further subclass, theso-called Bayesian regression methods such as, for example, kriging [Astatistical approach to some mine valuations and allied problems at theWitwatersrand, D. G. Krige, 1951; The intrinsic random functions, andtheir applications, G. Matheron, Adv. Appl. Prob., 5, pp 439-468, 1973],Gaussian processes [Evaluation of Gaussian Processes and other Methodsfor Non-Linear Regression, C. E. Rasmussen, 1996], sparse Gaussianprocesses [Sparse Gaussian processes using pseudo-inputs. E. Snelson andZ. Ghahramani, Advances in Neural Information Processing Systems 18, pp1259-1266, 2006], and others.

Parametric models require an a priori postulate of the relationshipbetween the input variables and the output variables that are to bemodeled. Such methods include, for example, physical modeling, linear orpolynomial regression, or also neural networks. The goal here is todetermine optimum parameters on the basis of the postulate so that thepostulated model comes as close as possible to the function beingmodeled. An incorrect or insufficient postulate thus also causes themodel to fail. Nonparametric modeling, on the other, hand, such asBayesian regression methods, requires no such postulate. Instead of themodel being limited to one specific postulate, all or very manyhypotheses can also be considered.

Bayesian regression is a data-based method, i.e. training points as wellas associated output values are needed in order to create a model. Fromthese, models are then created; this is essentially tantamount tostoring the training data and determining abstract “hyperparameters”that parameterize the space of the random functions and effectivelyweight the influence of the individual training points on the latermodel prediction. The kernel function K discussed below, which is thebasis for generating the random function (the Gaussian process), is theso-called quadratic exponential function.

The fundamentals of Bayesian regression may be found in “GaussianProcesses for Machine Learning,” C. E. Rasmussen and C. Williams, MITPress, 2006. The basic formula for calculating the prediction v at pointu is:

$v = {\sum\limits_{i = 1}^{N}{\left( Q_{y} \right)_{i}\sigma_{f}{\exp\left( {{- \frac{1}{2}}{\sum\limits_{d = 1}^{D}\frac{\left( {\left( x_{i} \right)_{d} - u_{d}} \right)^{2}}{l_{d}}}} \right)}}}$where v denotes the prediction of the output variable, x denotestraining data, u the test point (input variable), σ and l areexemplifying hyperparameters, and Q denotes a vector from modeltraining. The summations run over D dimensions of the input data ortraining data, and over N training data. The input variable u can beincorporated into the calculation in normalized form.

The formula for calculating the model variance σ_v for the outputvariable is:

$\left( k_{u} \right)_{i} = {\sigma_{f}{\exp\left( {{- \frac{1}{2}}{\sum\limits_{d = 1}^{D}\frac{\left( {\left( x_{i} \right)_{d} - u_{d}} \right)^{2}}{l_{d}}}} \right)}}$$\sigma_{v} = {\sigma_{f} - {\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}{\left( k_{u} \right)_{i}{Q_{ij}\left( k_{u} \right)}_{j}}}} - \sigma_{n}^{2}}$

Possible implementations of Bayesian regression exist in the case ofkriging, by reducing the so-called hyperparameters. Other Bayesianregression methods could alternatively also introduce additionalhyperparameters or work with other kernel functions, which could also dowithout exponential functions. In the case of sparse GP, virtualtraining points (x_(i)) are introduced, which can be distributed inproblem-specific fashion over the input space and can thus reduce thenumber N of training points required. Alternative approaches, such as KDtrees, use limit values for the internal distances (internal sum overD), and efficient memory structures, in order to allow summaryestimation of the influence of groups of lower-relevance trainingpoints. The result is to further reduce the number of terms (externalsum over N) that need to be calculated.

In particular, the formulas recited above can be reduced to a simplerform:

$\hat{v} = {\sum\limits_{i = 1}^{N}{\left( Q_{yA} \right)_{i}{\exp\left( {{\sum\limits_{d = 1}^{D}{\left( {\hat{x}}_{i} \right)_{d}{\hat{u}}_{d}}} - {\sum\limits_{d = 1}^{D}{\hat{u}}_{d}^{2}}} \right)}}}$

The sparse Gaussian process is particularly suitable, since it allowslarger data volumes to be processed and increases the prediction speed,since the number of virtual training points is typically smaller thanthe number of original data.

There are numerous possibilities for expanding the algorithms, allowingthe methods to be used with large data volumes, for example, so that(depending on the accuracy requirement) either a fast but less accurateresult is returned, or else a more accurate result that nevertheless ismore complex to calculate; in other words, it is possible to adapt theaccuracy of the model prediction to the available computation time. [E.Snelson and Z. Ghahramani, Advances in Neural Information ProcessingSystems 18, pp 1259-1266, 2006] [Fast Gaussian process regression usingKD-trees, Y. Shen, A. Ng, and M. Seeger, in Advances in NeuralInformation Processing Systems 18, pages 1227-1234, 2006]

Bayesian regression methods such as, for example, kriging, Gaussianprocesses, sparse Gaussian processes, etc. are now to be used on thecontrol device of a vehicle for predicting engine-relevant parameters(e.g. combustion-engineering variables, air system variables, etc.) foropen- and/or closed-loop control purposes.

FIG. 1 schematically shows a control device in a vehicle. Control device1 possesses a computation unit 10, a memory 12, an input 11, an output13, and a logic circuit 14. Through input 11, the control devicereceives signals from outside the control device, for example fromsensors or other control devices, computation units, or other modules101 to 104. These variables are referred to here and hereinafter as“input variables,” and can represent, for example, temperature signals,rotation speed signals, quantity signals, etc. Memory unit 12 storesvalues that were determined for specific variables in test measurementsoffline, i.e. before the operation of control device 1 or of thevehicle, and were stored in memory unit 12. These values are referred tohere and hereinafter as “training values.” The terms “offline” or“before operation” determine a phase in which control device 1 is notbeing used, during normal operation of the vehicle, for real-timeclosed- and open-loop control tasks (“online,” “during operation”), butin which vehicle functions relevant to the control device are tested,calibrated, and determined, for example in an application of the controldevice an automotive supplier's or automobile manufacturer's factory, ina repair shop, or during testing operation.

Parameters and variables that have been received or calculated bycontrol device 1, and are likewise included among the input variables,can also be stored in memory 12. Computation unit 10 executes a softwareprogram to determine one or more output variables for performing theopen- or closed-loop control functions of control device 1. “Outputvariables” refers here to the variables necessary for open-/closed-loopcontrol, which it is impossible or very complex to measure or determinedirectly in the vehicle and which are therefore determined from theavailable input variables. For this the control device carries out,during operation, a Bayesian regression over the training data that arestored in memory 12 and are relevant to the output variable to bedetermined, in consideration of the input variables relevant to theoutput to be determined. For this, calculation unit 10 can execute thealgorithms necessary for carrying out the regression in software, but itcan also offload computation steps to specialized hardware units such aslogic circuit 14 that is shown in FIG. 1 and is connected to thecomputation unit. The output that is determined, or an open- orclosed-loop control signal determined with it, is outputted via output13, e.g. to actuator 104.

The methods presented here are very well suited for use in dynamicmodeling with autoregressive approaches. This expands the potentialapplications into areas in which, for example in a context of dynamicload changes, the transient behavior of the modeled variable isimportant (see Detailed Description). [Multiple-step ahead predictionfor non linear dynamic Systems—A Gaussian process treatment withpropagation of the uncertainty, A. Girard, C. E. Rasmussen, and R.Murray-Smith, in Advances in Neural Information Processing Systems 15,Cambridge, 2003] [Comprising Prior Knowledge in Dynamic Gaussian ProcessModels, K. Azman and J. Kocijan, International Conference on ComputerSystems and Technologies—CompSysTech 2005] [Gaussian process approachfor modeling of nonlinear systems, G. Gregorcic, G. Lightbody, inEngineering Applications of Artificial Intelligence 22, pages 522-533,2009] Dynamic modeling is a relative direct expansion of the proposedmethod. Whereas with classic quasi-steady-state modeling only thecurrent input values at time t can be used as inputs, dynamic modelingpermits on the one hand input values from the past, e.g. Xi(t−1), . . .Xi(t−m), in order to capture slow-acting input variables, and on theother hand the most recent output values, i.e. y(t−1), . . . y(t−m), inorder to model sluggish systems with memory effects. It is therebypossible to achieve better mapping of, for example, variables with amemory effect such as exhaust gas temperature, which is also dependenton past engine variables, or variables such as boost pressure, whichdepends on the turbocharger setting (which changes only slowly).

FIG. 2a shows a non-dynamic method in which current input variables 201to 205 are incorporated into a Bayesian regression 20 in order toidentify an output variable 206. FIG. 2b , on the other hand, shows adynamic modeling operation, in which current input variables 211 and212, input variables from the past 213 and 214, and the output variablefrom the past 216 are incorporated into Bayesian regression 21 in orderto identify the current output variable 215.

As mentioned, Bayesian regression methods are notable for beingso-called “black box” methods, i.e. these regression methods have, formodel creation, no input parameters other than the training datameasured offline. Without prior knowledge and with no parameterizationof the modeling algorithm by an applications engineer, they can easilymap high-dimensional nonlinear correlations based only on the measuredtraining data. In contrast to all other methods, the Bayesian regressionmodels can moreover, in addition to model prediction, also provide aconclusion as to model variance (model uncertainty) at the particularpoint being queried. This makes it possible to detect unforeseen systemstates and react to them accordingly. This case can occur, for example,if the system has not been completely excited when themeasurement/training points are recorded; for example, an engine for thereference measurements was operated only at low engine speeds. Thecharacteristics-diagram approach would, if applicable, proceed steadilywith the prediction, and so might a physical approach if inputcorrelations were reduced to characteristic curves or characteristicsdiagrams.

The additional model variance allows us to ascertain, on the controldevice, whether the model is suitable for making reliable predictions inthe current system state, as in the above example in which onlymeasurement/training points for low engine speeds were recorded in thetraining phase for an engine model, but the driver is driving in highengine-speed ranges. With the model variance of the Bayesian regressionit is possible, for example, to ascertain that the model is not reliablein this case, and one could switch over from a conventionalopen-/closed-loop control approach to a defensive open-/closed-loopcontrol approach for exceptional situations, so that the engine cannotbe damaged as a result of incorrect model outputs (fail-safe mode).Further actions, such as fault signal output, are also conceivable.

FIG. 3a schematically shows input variables 301 to 305 that areincorporated into a Bayesian regression 30 in order to identify outputvariable 306, from which an open-/closed-loop control signal 307 isdetermined by way of an open-/closed-loop control function 300. In FIG.3b , corresponding input variables 311 to 315 are incorporated into aBayesian regression 31. In addition to output variable 316, variance 318of output variable 316 is taken into consideration as open-/closed-loopcontrol proceeds. “Variance” is not to be understood here in thestrictly mathematical sense, but instead refers to the uncertainty,estimated by the regression, associated with the determination of theoutput variable. For this, on the basis of output variable 316 adefensive open-/closed-loop control function 310 and a conventionalopen-/closed-loop control function 320 are used, which respectivelydetermine a defensive open-/closed-loop control signal 317 and aconventional open-/closed-loop control signal 327. Depending on variance318, decision function 330 decides which of the two open-/closed-loopcontrol signals 317 and 327 is to be outputted as open-/closed-loopcontrol signal 337. With a high variance 318, in particular a variance318 that exceeds a specific threshold, the defensive open-/closed-loopcontrol signal 317 is selected, otherwise the conventionalopen-/closed-loop control signal 327.

The proposed Bayesian regression methods can be computed in the controldevice in real time, either in software or at least partly in specialhardware. They are outstandingly parallelizable, so that efficientimplementations are possible both on future standard multicorearchitectures and in dedicated hardware.

The relevant algorithm in C code for a software implementation couldread, for example, as follows:

float amu_asc_predict_single( float *pl, /* m_x (Dim. D) */ float *p2,/* f_x (Dim. D) */ float *p3, /* Qya (Dim. N) */ float pA, /* f_y(Dim. 1) */ float p5, /* m_y (Dim. 1) */ unsigned int p6 , /* N (Dim.1): Number of training points/passes through external loop. */ unsignedint p7, /* D (Dim. 1) */ unsigned intp8, /* NStart (Dim. 1): Startingvalue of external loop. First index = 0 */ float p9, /* vlnit (Dim. 1):Normally 0, except upon continuation: then intermediate sum of precedingloop iterations.*/ float *V, /* hatX (Dim N*D): Rescaled training points*/ float *u /* Test point tilde u (Dim. D) */ ) { float v = p9; float C= 0.0; float t; unsigned int k,j,i; for (k=0_(;) k < p7; k++) { u[k] =(u[k] − pl[k]) * p2[k]_(;) C += u[k] * u[k]_(;) } for (j=p8; j < p6;j++){ t = −C; i =j * p7; for (k=0; k < p7; k++) t += V[i+k] * u[k]_(;) v +=p3[j] * exp(t); } return v*p4 + p5; }

This reduced form is valid for kriging, Gaussian processes, and sparseGaussian processes. As is evident from the C code, the evaluation of themodel can be parallelized by distributing especially, but notexclusively, the loop through j (in the formula, the external sum overN) over multiple computation units (e.g. CPU1: sum p8 to t; CPU2: sumt+1 to p6), and then combining the sub-results again in a separate step(e.g. v1=CPU1, v2=CPU2; v=(v1+v2)−k*p5, where k=number of CPUs−1 (in theexample, k=1)).

Alternatively to a calculation in software, as described by way ofexample with the above source code, the necessary algorithms can also beimplemented entirely or partly in specialized hardware. For example, ahardware unit, in particular a logic circuit, that is associated with acomputation unit of the control device can be optimized for thecalculation of specific computation steps necessary for carrying out theBayesian regression. It is particularly advantageous in this context tooffload the execution of exponential function calculations to the logiccircuit.

FIG. 4 schematically shows, for this purpose, components of amicrocontroller of a control device and their connections. A computationunit or processor core 41 of a computation unit, a first global memoryunit 42, a second global memory unit 43, and a logic circuit 44 are eachconnected to communication connection 40, and by way of the latter caneach communicate with one another. Communication connection 40 can beprovided, for example, as a bus system. FIG. 4 divides communicationconnection 40 into two separate buses, for example with a bus bridge 46.Logic circuit 44 can be connected to a local memory unit 45.

Global memory units 42 and 43 can be configured, for example, as RAMmemories or flash memories. The provision of two global memories in FIG.4 is an optional configuration. Local memory 45 can be provided, forexample, as RAM memory or as a register, and is preferably visible inthe global address region. Bus bridge 46 shown in FIG. 4 is optional. Asdiscussed in more detail in the description below, in a particularembodiment of the invention it is also possible to omit local memory 45.The components of the microcontroller that are shown in FIG. 4 are notto be understood as exhaustive; in particular, further processor corescan also be provided.

Circuit arrangement 44 is configured to calculate an exponentialfunction or (since it is configurable) various exponential functions,and optionally the sum of exponential functions. It represents a statemachine that fetches input data from an input memory for calculation,calculates an exponential function in the course of the calculation,optionally calculates the sum of exponential functions in the necessaryloop iterations, by way of an execution control system in communicationwith the computation unit or with processor core 41 of themicrocontroller, and thus serves in a way as a hardware accelerator inthe performance of complex tasks of the microcontroller or calculationsof processor core 41. Logic circuit 44 is present here as a separatehardware component outside the processor.

Many approaches to the calculation of exponential functions in hardwarecircuits are known. For example, a BKM (Bajard/Kla/Muller) algorithm, aCORDIC algorithm, or known series developments for the approximation ofexponential functions can be used.

The logic circuit of the microcontroller herewith proposed can assistcalculations of the microcontroller by making available calculatedexponential functions, and can thus make possible faster, moreeconomical (in terms of cost and space requirement), moreenergy-efficient, and more reliable calculations of tasks of themicrocontroller for which the calculation of exponential functionsrepresents a sub-task. The capability of configuring the logic circuitcan result in particularly flexible but still efficient calculationassistance.

Because the logic circuit is present as a separate hardware componentoutside the processor, there are no direct correlations with theprocessor. The result is to avoid mutual influences on the executionspeed of the further processor functions. Execution of the software isnot directly influenced. Despite the limited functionality, thefunctionality that is implemented can nevertheless be utilized asflexibly as possible, and for that purpose is controlled by a softwareprocessor.

The logic circuit in the microcontroller can be used particularlyflexibly if it is configurable; for example, configuration data forpurposes of configuring it can be read out of a configuration datamemory. Such configuration data can be training data or hyperparameters,or can also refer to how the exponential function or the sums are to becalculated. It is also possible to stipulate that the logic circuitcalculates exponential functions of sums of variable length andsummands. The summation of different exponential functions can also beconfigured, such that for each of the exponential functions to besummed, the parameters and the number of exponential functions to besummed can be configurable. In addition, the configuration can alsorelated to the manner in which the exponential function is calculated,for example if various calculation paths are possible using the logiccircuit or if, for example, a parallelization of calculations within thelogic circuit is possible.

The configuration data can usefully be generated by the processor or bya further computation unit of the microcontroller, preferably as afunction of the task to be calculated or of specific vehicle data, andwritten into a configuration data memory to which the logic circuit hasaccess. The logic circuit is thus flexibly adaptable to the tasks beingcalculated, but also to further conditions.

For efficient implementation of the configuration, the logic circuit canpossess a connected (local) memory in which the configuration data arestored.

If savings are to be realized by not using a local memory, it can alsobe advantageous to store the configuration data in a global memory towhich the logic circuit can have, for example, direct memory access(DMA) so as to enable rapid and reliable configuration with thissolution as well.

FIG. 5 schematically shows the steps of a method for ascertaining outputvariables using a control device during driving operation, by Bayesianregression of training data determined prior to driving operation.

In the first step 51, training values for input variables and outputvariables are determined in an application phase by measurements beforeoperation (also referred to as “offline”), i.e. for example at thecontrol device manufacturer's or vehicle manufacturer's factory, andstored for access by the control device. The values thus determined areon the one hand values that can be transmitted by sensors to the controldevice during operation of the vehicle (online), or general values thatcan be determined by the control device, for example, indirectly bycalculations based on sensor data.

These are the input values of the model in online mode. On the otherhand they are values that cannot be determined directly for the controldevice during online operation of the control device or of the vehicle.Such values may, however, be needed by the control device in order tocontrol specific functions of the vehicle. These are the output valuesof the model in online mode.

Separating line 500 separates, for the method illustrated, the offlinemethod step 51 from the online method steps 52 to 54.

In the second method step 52, the control device receives inputvariables, e.g. from sensors.

In the third method step 53 the control device carries out, on the basisof the received and possibly further stored or computationallyascertained input values, a Bayesian regression based on the trainingvalues previously determined offline, and thereby identifies at leastone output variable and optionally a variance of the output variablefrom which a conclusion can be drawn as to the accuracy of the outputvariable determination. Within the control device the Bayesianregression can be carried out, for example, by one or more computationunits of the control device in software; sub-tasks of the calculation,however, in particular the calculation of exponential functions, canalso be offloaded to a hardware unit, in particular a logic circuit,optimized therefor.

In the fourth method step 54, the control device uses the determinedoutput variable, and optionally the variance thereof, to control inopen- or closed-loop fashion a function in the vehicle, for example byoutputting an open- or closed-loop control signal to an actuator.

During operation of the vehicle, the method is usually carried outrepeatedly in succession, in particular including for different outputvariables and on the basis of different input variables. Different suchmethods can also be carried out in entirely or partly parallel fashion.

What is claimed is:
 1. A control device in a vehicle, comprising: acalculation unit configured to calculate, during operation of thevehicle and on the basis of at least one input variable ascertainedduring operation of the vehicle, at least one output variable for acontrol system of a function of the vehicle, wherein the calculationunit is configured to perform the calculation of the at least one outputvariable using a Bayesian regression over training values ascertained,before operation, for the at least one output variable and the at leastone input variable; a computation unit configured to carry out theBayesian regression at least partly; and a logic circuit that isassociated with the computation unit of the control device, wherein thelogic circuit is optimized for the calculation of specific computationsteps of the Bayesian regression; wherein the at least one inputvariable ascertained during the operation of the vehicle represents atleast one of temperature signals, rotation speed signals, and quantitysignals, the at least one of temperature signals, rotation speedsignals, and quantity signals being received by the control device fromat least one of: (i) at least one sensor of the vehicle, (ii) at leastone other control device of the vehicle, and (iii) at least one moduleof the vehicle outside the control device; wherein the at least oneoutput variable includes at least one combustion engine-relevantparameter; and wherein the control device controls a combustion engineof the vehicle using the at least one combustion engine-relevantparameter during operation of the vehicle.
 2. The control device asrecited in claim 1, wherein the calculation unit implements the Bayesianregression as a kriging or a sparse Gaussian process.
 3. The controldevice as recited in claim 2, wherein the calculation unit determines avariance, ascertained from the Bayesian regression, of the outputvariable.
 4. The control device as recited in claim 3, furthercomprising: means for initiating a corrective action if the ascertainedvariance exceeds a predetermined threshold value.
 5. The control deviceas recited in claim 3, wherein for the calculation of the at least oneoutput variable, a Bayesian regression dynamically modeled byautoregression is carried out over the training values ascertainedbefore operation.
 6. The control device as recited in claim 1, whereinthe logic circuit associated with the computation unit is configured tocalculate exponential functions.
 7. The control device as recited inclaim 1, wherein the calculation unit implements the Bayesian regressionas a Gaussian process.
 8. The control device as recited in claim 7,wherein the calculation unit determines a variance, ascertained from theBayesian regression, of the output variable.
 9. The control device asrecited in claim 8, further comprising: means for initiating acorrective action if the ascertained variance exceeds a predeterminedthreshold value.
 10. The control device as recited in claim 8, whereinthe logic circuit associated with the computation element is configuredto calculate exponential functions.
 11. The control device as recited inclaim 1, wherein the calculation unit implements the Bayesian regressionas a kriging process.
 12. The control device as recited in claim 1,wherein the calculation unit implements the Bayesian regression as asparse Gaussian process.
 13. The control device as recited in claim 1,wherein the calculation unit is further configured to: determine avariance, ascertained from the Bayesian regression, for the at least oneoutput variable; wherein the control device is configured to control thevehicle according to a first control scheme, and when the ascertainedvariance exceeds a predetermined threshold value that indicates that amodel used for the Bayesian regression is not reliable, switch from thefirst control scheme to a second fail-safe control scheme and controlthe vehicle according to the second fail-safe control scheme whichprevents damage to the vehicle.
 14. A method for calculating by acontrol device in a vehicle, during operation of the vehicle and on thebasis of at least one input variable ascertained during operation, atleast one output variable for a control system of a function of thevehicle, comprising: ascertaining training values for the at least oneoutput variable and the at least one input variable before operation ofthe vehicle; during operation of the vehicle, receiving, by the controldevice, the at least one input variable ascertained during the operationof the vehicle including at least one of (i) temperature signals, (ii)rotation speed signals, and (iii) quantity signals, from at least oneof: (i) at least one sensor, (ii) at least one other control device, and(iii) at least one module outside the control device, the at least oneof the temperature signals, rotational signals, and quantity signalsbeing ascertained during the operation of the vehicle; during operationof the vehicle, performing the calculation of the at least one outputvariable using a Bayesian regression over the training values; duringoperation of the vehicle, carrying out the Bayesian regression at leastpartly with a computation unit of the control device; and optimizing alogic circuit that is associated with the computation unit of thecontrol device for the calculation of specific computation steps of theBayesian regression; wherein the at least one output variable includesat least one combustion engine-relevant parameter; and wherein themethod further comprises controlling, by the control device, acombustion engine of the vehicle using the at least one combustionengine-relevant parameter during the operation of the vehicle.
 15. Themethod as recited in claim 14, wherein a variance, ascertained from theBayesian regression, of the output variable is also taken intoconsideration for the control system.
 16. The method as recited in claim15, wherein the Bayesian regression is implemented as a kriging or asparse Gauss process and is dynamically modeled by autoregressioncarried out over the training values ascertained before operation. 17.The method as recited in claim 15, wherein the Bayesian regression isimplemented as a kriging process.
 18. The method as recited in claim 15,wherein the Bayesian regression is implemented as a sparse Gaussprocess.
 19. The method as recited in claim 15, wherein the Bayesianregression is implemented as a Gauss process.
 20. The method as recitedin claim 14, further comprising: determining a variance, ascertainedfrom the Bayesian regression, for the at least one output variable;controlling the vehicle according to a first control scheme; and whenthe ascertained variance exceeds a predetermined threshold value thatindicates that a model used for the Bayesian regression is not reliable,switching from the first control scheme to a second fail-safe controlscheme and controlling the vehicle according to the second fail-safecontrol scheme which prevents damage to the vehicle.